The technique can help scientists in economics, public health, and other fields understand whether to trust the results of their experiments.

By stacking multiple active components based on new materials on the back end of a computer chip, this new approach reduces the amount of energy wasted during computation.

Postdoc Zongyi Li, Associate Professor Tess Smidt, and seven additional alumni will be supported in the development of AI against difficult problems.

The speech-to-reality system combines 3D generative AI and robotic assembly to create objects on demand.

Founded by MIT alumni, the Pickle Robot Company has developed machines that can autonomously load and unload trucks inside warehouses and logistic centers.

This new technique enables LLMs to dynamically adjust the amount of computation they use for reasoning, based on the difficulty of the question.

With insect-like speed and agility, the tiny robot could someday aid in search-and-rescue missions.

Macro, a modeling tool developed by the MIT Energy Initiative, enables energy-system planners to explore options for developing infrastructure to support decarbonized, reliable, and low-cost power grids.

MIT CSAIL and LIDS researchers developed a mathematically grounded system that lets soft robots deform, adapt, and interact with people and objects, without violating safety limits.

AquaCulture Shock program, in collaboration with MIT-Scandinavia MISTI, offers international internships for AI and autonomy in aquaculture

At MITEI’s Fall Colloquium, General Motors’ battery development expert emphasized how affordability, accessibility, and commercialization can position the US as a leader in battery tech.

For PhD student Benjamin Manning, the future of work means grasping AI’s role on our behalf while transforming and accelerating social scientific discovery.

Large language models can learn to mistakenly link certain sentence patterns with specific topics — and may then repeat these patterns instead of reasoning.

BoltzGen generates protein binders for any biological target from scratch, expanding AI’s reach from understanding biology toward engineering it.

AI supports the clean energy transition as it manages power grid operations, helps plan infrastructure investments, guides development of novel materials, and more.

MIT neuroscientists find a surprising parallel in the ways humans and new AI models solve complex problems.

The virtual VideoCAD tool could boost designers’ productivity and help train engineers learning computer-aided design.

Industry leaders agree collaboration is key to advancing critical technologies.

Associate Professor Phillip Isola studies the ways in which intelligent machines “think,” in an effort to safely integrate AI into human society.

MIT faculty and MITEI member company experts address power demand from data centers.

MIT PhD students who interned with the MIT-IBM Watson AI Lab Summer Program are pushing AI tools to be more flexible, efficient, and grounded in truth.

The coding framework uses modular concepts and simple synchronization rules to make software clearer, safer, and easier for LLMs to generate.

A new approach developed at MIT could help a search-and-rescue robot navigate an unpredictable environment by rapidly generating an accurate map of its surroundings.

MIT’s Teaching Systems Lab, led by Associate Professor Justin Reich, is working to help educators by listening to and sharing their stories.

MIT PhD student and CSAIL researcher Justin Kay describes his work combining AI and computer vision systems to monitor the ecosystems that support our planet.

The FSNet system, developed at MIT, could help power grid operators rapidly find feasible solutions for optimizing the flow of electricity.

PhD student Miranda Schwacke explores how computing inspired by the human brain can fuel energy-efficient artificial intelligence.

Professors Facundo Batista and Dina Katabi, along with three additional MIT alumni, are honored for their outstanding professional achievement and commitment to service.

How the MIT-IBM Watson AI Lab is shaping AI-sociotechnical systems for the future.

To reduce waste, the Refashion program helps users create outlines for adaptable clothing, such as pants that can be reconfigured into a dress. Each component of these pieces can be replaced, rearranged, or restyled.

After being trained with this technique, vision-language models can better identify a unique item in a new scene.

Media Lab PhD student Kimaya Lecamwasam researches how music can shape well-being.

An algorithm can change the face of food assistance policy in the Global South, says MIT assistant professor and J-WAFS researcher Ali Aouad.

Acting as a “virtual spectrometer,” SpectroGen generates spectroscopic data in any modality, such as X-ray or infrared, to quickly assess a material’s quality.

Co-founded by an MIT alumnus, Watershed Bio offers researchers who aren’t software engineers a way to run large-scale analyses to accelerate biology.

Receiving the Robert A. Muh award, the technologist and author heralded a bright future for AI, breakthroughs in longevity, and more.

The MIT–MBZUAI Collaborative Research Program will unite faculty and students from both institutions to advance AI and accelerate its use in pressing scientific and societal challenges.

New tool from MIT CSAIL creates realistic virtual kitchens and living rooms where simulated robots can interact with models of real-world objects, scaling up training data for robot foundation models.

Assistant Professor Priya Donti’s research applies machine learning to optimize renewable energy.

Incorporating machine learning, MIT engineers developed a way to 3D print alloys that are much stronger than conventionally manufactured versions.

The approach combines physics and machine learning to avoid damaging disruptions when powering down tokamak fusion machines.

MIT CSAIL and McMaster researchers used a generative AI model to reveal how a narrow-spectrum antibiotic attacks disease-causing bacteria, speeding up a process that normally takes years.

Bakshi will help shape and scale entrepreneurship education and platform at MIT.

Optimized for generative AI, TX-GAIN is driving innovation in biodefense, materials discovery, cybersecurity, and other areas of research and development.

Explosive growth of AI data centers is expected to increase greenhouse gas emissions. Researchers are now seeking solutions to reduce these environmental harms.

The new “CRESt” platform could help find solutions to real-world energy problems that have plagued the materials science and engineering community for decades.

By enabling rapid annotation of areas of interest in medical images, the tool can help scientists study new treatments or map disease progression.

With SCIGEN, researchers can steer AI models to create materials with exotic properties for applications like quantum computing.

This year’s delta v summer accelerator offered an up-close look at how AI is changing the process of building a startup.

At the inaugural MIT Generative AI Impact Consortium Symposium, researchers and business leaders discussed potential advancements centered on this powerful technology.